Xiaodong Qu

I am an Assistant Professor of Practice in the Computer Science Department at George Washington University.

My research interests are Machine Learning and Brain-Computer Interfaces.

I was a Visiting Assistant Professor of Computer Science at Swarthmore College, 2021-23.

I received my Ph.D. in Computer Science from Brandeis University. My advisor is Professor Timothy J Hickey.

Here is the pronunciation of my first name and last name.

x.qu@gwu.edu

Zoom Link for Office Hours and Research Meetings

More on our CS Faculty Page

Machine Learning (Fall 2024, Fall 2023, Fall 2022, Spring 2022)

Master’s Thesis (Fall 2024 to Spring 2025)

Senior Capstone Design Project (Fall 2024 to Spring 2025, Fall 2023 to Spring 2024)

Independent Research Project (Fall 2022, Spring 2023)

Algorithms (Spring 2024, Spring 2025)

Intro to Computer Science (Fall 2021, Spring 2023)

I am actively recruiting motivated students interested in Machine Learning or Neuroscience.

Please see this Research Talk Fall 2022 for an overview of my recent research.

Our research lab currently has fifteen students (and fifteen Brain-Computer Interfaces headsets).

My publications are here: Google Scholar and Research Gate.

Congratulations!

Three papers at SIGKDD 2024, KDD 2024 Undergraduate Consortium.

Evaluating Generative AI Models on Summarizing Undergraduate Data Science Research Papers Tse Saunders, Nawwaf Aleisa, Juliet Wield, Joshua Sherwood, Xiaodong Qu

Optimizing Multichannel EEG Data: An Investigation of Current EEG Data Compression Methods Andrews Damoah, Teng Liang

Enhancing EEG Data Quality: A Comprehensive Review of Outlier Detection and Cleaning Methods Sofia Utoft, Jingwen Dou, Jade Wu

Eleven papers at HCI International 2024.

AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection Aditya Singh, Pavan Reddy

Enhancing Representation Learning of EEG Data with Masked Autoencoders Yifei Zhou, Sitong Liu

Whisper+AASIST for DeepFake Audio Detection Qian Luo, Kalyani Vinayagam Sivasundari

Advancing EEG-Based Gaze Prediction: Depthwise Separable Convolution and Pre-Processing Enhancements in EEGViT Matthew Key, Tural Mehtiyev, Xiaodong Qu

Enhancing Eye-Tracking Performance through Multi-Task Learning Transformer Weigeng Li, Neng Zhou, Xiaodong Qu

Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data Chuhui Qiu, Bugao Liang, Matthew Key

Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression Eric Modesitt, Haicheng Yin, Williams Huang Wang, Brian Lu

Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study Xiaodong Qu, Matthew Key, Qian Luo, Chuhui Qiu

EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures Teng Liang, Andrews Damoah

Refining Human-Data Interaction: Advanced Techniques for EEGEyeNet Dataset Precision Jade Wu, Jingwen Dou, Sofia Utoft

Optimizing Self-Paced Learning in Machine Learning Education for Working Professionals: Strategies, Trends, and Insights Peiyan Liu

Two more papers at SIGKDD 2023, KDD 2023 Undergraduate Consortium.

Ruiqi Yang and Eric J Modesitt

ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data

Nathan K Murungi, Michael V Pham, Xufeng Dai, and I

Trends in Electroencephalogram (EEG) Classification using Machine Learning: A Review for Undergraduate Researchers

Two papers at last year SIGKDD 2022, KDD 2022 Undergraduate Consortium, SIGKDD, the top conference in Data Mining and Analysis.

Abdelrahman Abdelmonsef and Brian Xiang

Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers

Guangyao Dou and Zheng Zhou

EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis

Three Full-Time Research Students, Summer 2022:

Abdelrahman Abdelmonsef, Brian Xiang, and Tianyi (Jonathan) Wang.

Funded by Swarthmore Summer Research Opportunities

More Student Papers

(Selected from HCI International, one of the top twenty conferences in Human Computer Interaction)

S014: Advances in Augmented Cognition - I

It’s Easy as ABC Framework for User Feedback, Sydney Levy, Alexandra Fischmann

High-Powered Ocular Artifact Detection with C-LSTM-E, Ian McDiarmid-Sterling, Luca Cerbin

Single-Subject vs. Cross-Subject Motor Imagery Models, Joseph Geraghty, George Schoettle

ML vs DL: Accuracy and Testing Runtime Trade-offs in BCI, Anarsaikhan Tuvshinjargal, Elliot Kim

S045: Advances in Augmented Cognition - IΙ

Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift, Brian Xiang, Abdelrahman Abdelmonsef

CNN with Self-Attention in EEG Classification, Xuduo Wang, Ziji Wang

Optimizing ML Algorithms under CSP and Riemannian Covariance in MI-BCIs, Yang Windhorse, Nader Almadbooh

S089: AI for Human Cognition, Emotions, and Physiology

Using Machine Learning to Determine Optimal Sleeping Schedules of Individual College Students, Orlando Azuara Hernandez, Zachary Gillette

Hierarchical Binary Classifiers for Sleep Stage Classification, Kenneth Barkdoll, Erebus Oh

(Selected, more details are on my Google Scholar page and Research Gate page)

Yi, Long, and Xiaodong Qu. "Attention-Based CNN Capturing EEG Recording’s Average Voltage and Local Change." In International Conference on Human-Computer Interaction, pp. 448-459. Springer, Cham, 2022. Paper, Slides

Wang, Ruyang, and Xiaodong Qu. "EEG Daydreaming, A Machine Learning Approach to Detect Daydreaming Activities." In International Conference on Human-Computer Interaction, pp. 202-212. Springer, Cham, 2022. Paper, Slides

Dou, Guangyao, Zheng Zhou, and Xiaodong Qu. "Time Majority Voting, a PC-based EEG Classifier for Non-expert Users." In International Conference on Human-Computer Interaction, 2022, Late Breaking Work. PDF File, Slides

Zhou, Zheng, Guangyao Dou, and Xiaodong Qu. "BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students." In International Conference on Human-Computer Interaction, 2022, Late Breaking Work. Paper, Poster

Qu, Xiaodong, and Timothy J. Hickey. "EEG4Home: A Human-In-The-Loop Machine Learning Model for EEG-Based BCI." In International Conference on Human-Computer Interaction, pp. 162-172. Springer, Cham, 2022. PDF File, Slides

Qu, Xiaodong, Peiyan Liu, Zhaonan Li, and Timothy Hickey. "Multi-class Time Continuity Voting for EEG Classification." In International Conference on Brain Function Assessment in Learning, pp. 24-33. Springer, Cham, 2020. Best Paper Award, PDF File

Qu, Xiaodong, Qingtian Mei, Peiyan Liu, and Timothy Hickey. "Using EEG to distinguish between writing and typing for the same cognitive task." In International Conference on Brain Function Assessment in Learning, pp. 66-74. Springer, Cham, 2020. PDF File

Qu, Xiaodong, Saran Liukasemsarn, Jingxuan Tu, Amy Higgins, Timothy J. Hickey, and Mei-Hua Hall. "Identifying clinically and functionally distinct groups among healthy controls and first episode psychosis patients by clustering on EEG patterns." Frontiers in psychiatry (2020): 938. PDF File

Qu, Xiaodong, Yixin Sun, Robert Sekuler, and Timothy Hickey. "EEG markers of STEM learning." In 2018 IEEE Frontiers in Education Conference (FIE), pp. 1-9. IEEE, 2018. PDF File

Qu, Xiaodong, Mercedes Hall, Yile Sun, Robert Sekuler, and Timothy J. Hickey. "A Personalized Reading Coach using Wearable EEG Sensors." Presentation In Analytics in Education Environments (A2E2018). PDF File

Program board member for subconferences of HCI International, one of the top twenty conferences in Human Computer Interaction.

Chair the following sessions in HCI International.

Department Representative (Computer Science, 2022-23) of Sigma Xi